| |JANUARY 20269AI's Role in Teaching: Enhancement, Not ReplacementAI will enhance, not replace, traditional teaching. Effective teaching relies on timeless principles: clear objectives, engaging experiences, inclusivity, student-centered learning, feedback, critical thinking, real-world connections, and adaptability. AI doesn't change these. The challenge is teachers' limited time and attention. AI excels here, providing instant, personalized feedback at scale. With AI handling routine tasks, educators can focus on interactive, social learning. Because learning is social, AI allows teachers to engage more meaningfully and be free from administrative burdens. AI's full impact is unfolding, but it promises more time for deeper discussions, collaboration, and mentoring--enriching the human side of education.Post-Pandemic Teaching: Transition, Not FailureInstitutions returning to pre-pandemic models aren't necessarily rejecting digital methods but instead seeking the social engagement of in-person learning. This isn't failure, but transition. The rise of AI during this period has further disrupted the landscape, leading to overwhelm and a return to familiar methods for stability. It's too early for a clear path. Institutions need to assess their specific contexts. Critically, any pre/post-pandemic teaching discussion must acknowledge AI's massive, ongoing impact. This isn't a failure to progress but a recognition that education is undergoing multiple, overlapping shifts. The integration of digital, hybrid, and AI strategies is still unfolding.Measuring Online Learning Impact: Beyond Simple MetricsThis reveals a bias: online programs often face stricter effectiveness scrutiny than on-campus ones. The real issue is how we measure learning. We've long relied on imperfect proxies--tests, papers, projects--despite their limitations. If in-person programs are assumed to produce learning, online programs may try to show equivalent outcomes. However, this ignores that online and face-to-face students often differ in background, challenges, and needs. Direct outcome comparisons assume identical conditions, which is rarely true. Instead of proving online equivalence, we should ask: How do we measure authentic learning in any modality? How can we move beyond outdated proxies to assess actual knowledge, skills, and critical thinking? The issue is redefining learning effectiveness as a whole.Learner Engagement's Future: AI & Faculty CollaborationLearner engagement in digital education is evolving with AI-driven, individualized learning. As students use AI for content, faculty will focus on application. Instead of delivering content, they'll guide deeper engagement, encouraging critical analysis, synthesis, and real-world application. Faculty will shape the experience by defining objectives and creating active engagement opportunities: discussions, projects, and collaboration. While AI personalizes pathways, faculty provide intellectual and social scaffolding, ensuring learning leads to fundamental understanding and practical skills.Navigating the Digital Shift: Embrace TransformationMy advice to university leaders navigating the digital shift: move beyond assimilation to accommodation. Many are currently using AI to enhance existing models. Once comfortable with AI's capabilities, we must rethink and reimagine education. We must explore new models and methods AI enables, not just improve current ones. AI's potential is transformation, not just efficiency, shaping a more adaptive, personalized, and innovative future. AI's potential is transformation, not just efficiency, shaping a more adaptive, personalized, and innovative future.
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